from collections import defaultdict
import contextlib
import os
import datetime
from concurrent import futures
import time
import json
from absl import app, flags
from accelerate import Accelerator
from ml_collections import config_flags
from accelerate.utils import set_seed, ProjectConfiguration
from accelerate.logging import get_logger
from diffusers import StableDiffusion3Pipeline, FlowMatchEulerDiscreteScheduler
from diffusers.utils.torch_utils import is_compiled_module
from diffusers.training_utils import compute_density_for_timestep_sampling
import numpy as np
import flow_grpo.prompts
import flow_grpo.rewards
from flow_grpo.stat_tracking import PerPromptStatTracker
from flow_grpo.diffusers_patch.sd3_pipeline_with_logprob import pipeline_with_logprob
from flow_grpo.diffusers_patch.train_dreambooth_lora_sd3 import encode_prompt
import torch
import torch.nn.functional as F
import wandb
from functools import partial
import tqdm
import tempfile
from PIL import Image
from peft import LoraConfig, get_peft_model, PeftModel
import random
from torch.utils.data import Dataset, DataLoader, Sampler
from flow_grpo.ema import EMAModuleWrapper

tqdm = partial(tqdm.tqdm, dynamic_ncols=True)


FLAGS = flags.FLAGS
config_flags.DEFINE_config_file("config", "config/base.py", "Training configuration.")

logger = get_logger(__name__)

class TextPromptDataset(Dataset):
    def __init__(self, dataset, split='train'):
        self.file_path = os.path.join(dataset, f'{split}.txt')
        with open(self.file_path, 'r') as f:
            self.prompts = [line.strip() for line in f.readlines()]
        
    def __len__(self):
        return len(self.prompts)
    
    def __getitem__(self, idx):
        return {"prompt": self.prompts[idx], "metadata": {}}

    @staticmethod
    def collate_fn(examples):
        prompts = [example["prompt"] for example in examples]
        metadatas = [example["metadata"] for example in examples]
        return prompts, metadatas

class GenevalPromptDataset(Dataset):
    def __init__(self, dataset, split='train'):
        self.file_path = os.path.join(dataset, f'{split}_metadata.jsonl')
        with open(self.file_path, 'r', encoding='utf-8') as f:
            self.metadatas = [json.loads(line) for line in f]
            self.prompts = [item['prompt'] for item in self.metadatas]
        
    def __len__(self):
        return len(self.prompts)
    
    def __getitem__(self, idx):
        return {"prompt": self.prompts[idx], "metadata": self.metadatas[idx]}

    @staticmethod
    def collate_fn(examples):
        prompts = [example["prompt"] for example in examples]
        metadatas = [example["metadata"] for example in examples]
        return prompts, metadatas

class DistributedKRepeatSampler(Sampler):
    def __init__(self, dataset, batch_size, k, num_replicas, rank, seed=0):
        self.dataset = dataset
        self.batch_size = batch_size  # Batch size per replica
        self.k = k                    # Number of repetitions per sample
        self.num_replicas = num_replicas  # Total number of replicas
        self.rank = rank              # Current replica rank
        self.seed = seed              # Random seed for synchronization
        
        # Compute the number of unique samples needed per iteration
        self.total_samples = self.num_replicas * self.batch_size
        assert self.total_samples % self.k == 0, f"k can not divide n*b, k{k}-num_replicas{num_replicas}-batch_size{batch_size}"
        self.m = self.total_samples // self.k  # Number of unique samples
        self.epoch = 0

    def __iter__(self):
        while True:
            # Generate a deterministic random sequence to ensure all replicas are synchronized
            g = torch.Generator()
            g.manual_seed(self.seed + self.epoch)
            
            # Randomly select m unique samples
            indices = torch.randperm(len(self.dataset), generator=g)[:self.m].tolist()
            
            # Repeat each sample k times to generate n*b total samples
            repeated_indices = [idx for idx in indices for _ in range(self.k)]
            
            # Shuffle to ensure uniform distribution
            shuffled_indices = torch.randperm(len(repeated_indices), generator=g).tolist()
            shuffled_samples = [repeated_indices[i] for i in shuffled_indices]
            
            # Split samples to each replica
            per_card_samples = []
            for i in range(self.num_replicas):
                start = i * self.batch_size
                end = start + self.batch_size
                per_card_samples.append(shuffled_samples[start:end])
            
            # Return current replica's sample indices
            yield per_card_samples[self.rank]
    
    def set_epoch(self, epoch):
        self.epoch = epoch  # Used to synchronize random state across epochs


def compute_text_embeddings(prompt, text_encoders, tokenizers, max_sequence_length, device):
    with torch.no_grad():
        prompt_embeds, pooled_prompt_embeds = encode_prompt(
            text_encoders, tokenizers, prompt, max_sequence_length
        )
        prompt_embeds = prompt_embeds.to(device)
        pooled_prompt_embeds = pooled_prompt_embeds.to(device)
    return prompt_embeds, pooled_prompt_embeds


def copy_learner_to_ref(transformer):
    for name, param in transformer.named_parameters():
        if "learner" in name:
            ref_name = name.replace("learner", "ref")
            ref_param = dict(transformer.named_parameters())[ref_name]
            ref_param.data.copy_(param.data)


def calculate_zero_std_ratio(prompts, gathered_rewards):
    """
    Calculate the proportion of unique prompts whose reward standard deviation is zero.
    
    Args:
        prompts: List of prompts.
        gathered_rewards: Dictionary containing rewards, must include the key 'ori_avg'.
        
    Returns:
        zero_std_ratio: Proportion of prompts with zero standard deviation.
        prompt_std_devs: Mean standard deviation across all unique prompts.
    """
    # Convert prompt list to NumPy array
    prompt_array = np.array(prompts)
    
    # Get unique prompts and their group information
    unique_prompts, inverse_indices, counts = np.unique(
        prompt_array, 
        return_inverse=True,
        return_counts=True
    )
    
    # Group rewards for each prompt
    grouped_rewards = gathered_rewards['ori_avg'][np.argsort(inverse_indices)]
    split_indices = np.cumsum(counts)[:-1]
    reward_groups = np.split(grouped_rewards, split_indices)
    
    # Calculate standard deviation for each group
    prompt_std_devs = np.array([np.std(group) for group in reward_groups])
    
    # Calculate the ratio of zero standard deviation
    zero_std_count = np.count_nonzero(prompt_std_devs == 0)
    zero_std_ratio = zero_std_count / len(prompt_std_devs)
    
    return zero_std_ratio, prompt_std_devs.mean()


def get_sigmas(noise_scheduler, timesteps, accelerator, n_dim=4, dtype=torch.float32):
    sigmas = noise_scheduler.sigmas.to(device=accelerator.device, dtype=dtype)
    schedule_timesteps = noise_scheduler.timesteps.to(accelerator.device)
    timesteps = timesteps.to(accelerator.device)
    step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]

    sigma = sigmas[step_indices].flatten()
    while len(sigma.shape) < n_dim:
        sigma = sigma.unsqueeze(-1)
    return sigma
        

def eval(pipeline, test_dataloader, text_encoders, tokenizers, config, accelerator, global_step, reward_fn, executor, autocast, num_train_timesteps, ema, transformer_trainable_parameters):
    pipeline.transformer.set_adapter("learner")
    if config.train.ema:
        ema.copy_ema_to(transformer_trainable_parameters, store_temp=True)
    neg_prompt_embed, neg_pooled_prompt_embed = compute_text_embeddings([""], text_encoders, tokenizers, max_sequence_length=128, device=accelerator.device)

    sample_neg_prompt_embeds = neg_prompt_embed.repeat(config.sample.test_batch_size, 1, 1)
    sample_neg_pooled_prompt_embeds = neg_pooled_prompt_embed.repeat(config.sample.test_batch_size, 1)

    # test_dataloader = itertools.islice(test_dataloader, 2)
    all_rewards = defaultdict(list)
    for test_batch in tqdm(
            test_dataloader,
            desc="Eval: ",
            disable=not accelerator.is_local_main_process,
            position=0,
        ):
        prompts, prompt_metadata = test_batch
        prompt_embeds, pooled_prompt_embeds = compute_text_embeddings(
            prompts, 
            text_encoders, 
            tokenizers, 
            max_sequence_length=128, 
            device=accelerator.device
        )
        # The last batch may not be full batch_size
        if len(prompt_embeds)<len(sample_neg_prompt_embeds):
            sample_neg_prompt_embeds = sample_neg_prompt_embeds[:len(prompt_embeds)]
            sample_neg_pooled_prompt_embeds = sample_neg_pooled_prompt_embeds[:len(prompt_embeds)]
        with autocast():
            with torch.no_grad():
                images, _, _ = pipeline_with_logprob(
                    pipeline,
                    prompt_embeds=prompt_embeds,
                    pooled_prompt_embeds=pooled_prompt_embeds,
                    negative_prompt_embeds=sample_neg_prompt_embeds,
                    negative_pooled_prompt_embeds=sample_neg_pooled_prompt_embeds,
                    num_inference_steps=config.sample.eval_num_steps,
                    guidance_scale=config.sample.guidance_scale,
                    output_type="pt",
                    height=config.resolution,
                    width=config.resolution, 
                    noise_level=0,
                )
        rewards = executor.submit(reward_fn, images, prompts, prompt_metadata, only_strict=False)
        # yield to to make sure reward computation starts
        time.sleep(0)
        rewards, reward_metadata = rewards.result()

        for key, value in rewards.items():
            rewards_gather = accelerator.gather(torch.as_tensor(value, device=accelerator.device)).cpu().numpy()
            all_rewards[key].append(rewards_gather)
    
    last_batch_images_gather = accelerator.gather(torch.as_tensor(images, device=accelerator.device)).cpu().numpy()
    last_batch_prompt_ids = tokenizers[0](
        prompts,
        padding="max_length",
        max_length=256,
        truncation=True,
        return_tensors="pt",
    ).input_ids.to(accelerator.device)
    last_batch_prompt_ids_gather = accelerator.gather(last_batch_prompt_ids).cpu().numpy()
    last_batch_prompts_gather = pipeline.tokenizer.batch_decode(
        last_batch_prompt_ids_gather, skip_special_tokens=True
    )
    last_batch_rewards_gather = {}
    for key, value in rewards.items():
        last_batch_rewards_gather[key] = accelerator.gather(torch.as_tensor(value, device=accelerator.device)).cpu().numpy()

    all_rewards = {key: np.concatenate(value) for key, value in all_rewards.items()}
    if accelerator.is_main_process:
        with tempfile.TemporaryDirectory() as tmpdir:
            num_samples = min(15, len(last_batch_images_gather))
            # sample_indices = random.sample(range(len(images)), num_samples)
            sample_indices = range(num_samples)
            for idx, index in enumerate(sample_indices):
                image = last_batch_images_gather[index]
                pil = Image.fromarray(
                    (image.transpose(1, 2, 0) * 255).astype(np.uint8)
                )
                pil = pil.resize((config.resolution, config.resolution))
                pil.save(os.path.join(tmpdir, f"{idx}.jpg"))
            sampled_prompts = [last_batch_prompts_gather[index] for index in sample_indices]
            sampled_rewards = [{k: last_batch_rewards_gather[k][index] for k in last_batch_rewards_gather} for index in sample_indices]
            for key, value in all_rewards.items():
                print(key, value.shape)
            wandb.log(
                {
                    "eval_images": [
                        wandb.Image(
                            os.path.join(tmpdir, f"{idx}.jpg"),
                            caption=f"{prompt:.1000} | " + " | ".join(f"{k}: {v:.2f}" for k, v in reward.items() if v != -10),
                        )
                        for idx, (prompt, reward) in enumerate(zip(sampled_prompts, sampled_rewards))
                    ],
                    **{f"eval_reward_{key}": np.mean(value[value != -10]) for key, value in all_rewards.items()},
                },
                step=global_step,
            )
    if config.train.ema:
        ema.copy_temp_to(transformer_trainable_parameters)

def unwrap_model(model, accelerator):
    model = accelerator.unwrap_model(model)
    model = model._orig_mod if is_compiled_module(model) else model
    return model

def save_ckpt(save_dir, transformer, global_step, accelerator, ema, transformer_trainable_parameters, config):
    save_root = os.path.join(save_dir, "checkpoints", f"checkpoint-{global_step}")
    save_root_lora = os.path.join(save_root, "lora")
    os.makedirs(save_root_lora, exist_ok=True)
    if accelerator.is_main_process:
        if config.train.ema:
            ema.copy_ema_to(transformer_trainable_parameters, store_temp=True)
        unwrap_model(transformer, accelerator).base_model.model.save_pretrained(save_root_lora)
        if config.train.ema:
            ema.copy_temp_to(transformer_trainable_parameters)

def main(_):
    # basic Accelerate and logging setup
    config = FLAGS.config

    unique_id = datetime.datetime.now().strftime("%Y.%m.%d_%H.%M.%S")
    if not config.run_name:
        config.run_name = unique_id
    else:
        config.run_name += "_" + unique_id

    # number of timesteps within each trajectory to train on
    num_train_timesteps = int(config.sample.num_steps * config.train.timestep_fraction)

    accelerator_config = ProjectConfiguration(
        project_dir=os.path.join(config.logdir, config.run_name),
        automatic_checkpoint_naming=True,
        total_limit=config.num_checkpoint_limit,
    )

    accelerator = Accelerator(
        # log_with="wandb",
        mixed_precision=config.mixed_precision,
        project_config=accelerator_config,
        gradient_accumulation_steps=config.train.gradient_accumulation_steps * num_train_timesteps,
    )
    if accelerator.is_main_process:
        wandb.init(project="flow_grpo")
        # accelerator.init_trackers(
        #     project_name="flow-grpo",
        #     config=config.to_dict(),
        #     init_kwargs={"wandb": {"name": config.run_name}},
        # )
    logger.info(f"\n{config}")

    # set seed (device_specific is very important to get different prompts on different devices)
    set_seed(config.seed, device_specific=True)

    # load scheduler, tokenizer and models.
    pipeline = StableDiffusion3Pipeline.from_pretrained(
        config.pretrained.model
    )
    # freeze parameters of models to save more memory
    pipeline.vae.requires_grad_(False)
    pipeline.text_encoder.requires_grad_(False)
    pipeline.text_encoder_2.requires_grad_(False)
    pipeline.text_encoder_3.requires_grad_(False)
    pipeline.transformer.requires_grad_(not config.use_lora)

    text_encoders = [pipeline.text_encoder, pipeline.text_encoder_2, pipeline.text_encoder_3]
    tokenizers = [pipeline.tokenizer, pipeline.tokenizer_2, pipeline.tokenizer_3]

    # disable safety checker
    pipeline.safety_checker = None
    # make the progress bar nicer
    pipeline.set_progress_bar_config(
        position=1,
        disable=not accelerator.is_local_main_process,
        leave=False,
        desc="Timestep",
        dynamic_ncols=True,
    )

    # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora transformer) to half-precision
    # as these weights are only used for inference, keeping weights in full precision is not required.
    inference_dtype = torch.float32
    if accelerator.mixed_precision == "fp16":
        inference_dtype = torch.float16
    elif accelerator.mixed_precision == "bf16":
        inference_dtype = torch.bfloat16

    # Move vae and text_encoder to device and cast to inference_dtype
    pipeline.vae.to(accelerator.device, dtype=torch.float32)
    pipeline.text_encoder.to(accelerator.device, dtype=inference_dtype)
    pipeline.text_encoder_2.to(accelerator.device, dtype=inference_dtype)
    pipeline.text_encoder_3.to(accelerator.device, dtype=inference_dtype)
    
    pipeline.transformer.to(accelerator.device)

    if config.use_lora:
        # Set correct lora layers
        target_modules = [
            "attn.add_k_proj",
            "attn.add_q_proj",
            "attn.add_v_proj",
            "attn.to_add_out",
            "attn.to_k",
            "attn.to_out.0",
            "attn.to_q",
            "attn.to_v",
        ]
        transformer_lora_config = LoraConfig(
            r=32,
            lora_alpha=64,
            init_lora_weights="gaussian",
            target_modules=target_modules,
        )
        if config.train.lora_path:
            pipeline.transformer = PeftModel.from_pretrained(pipeline.transformer, config.train.lora_path)
            # After loading with PeftModel.from_pretrained, all parameters have requires_grad set to False. You need to call set_adapter to enable gradients for the adapter parameters.
            pipeline.transformer.set_adapter("default")
        else:
            pipeline.transformer = get_peft_model(pipeline.transformer, transformer_lora_config, adapter_name="learner")
            pipeline.transformer = get_peft_model(pipeline.transformer, transformer_lora_config, adapter_name="ref")
            pipeline.transformer.set_adapter("learner")
            
    
    transformer = pipeline.transformer
    transformer_trainable_parameters = []
    for name, param in transformer.named_parameters():
        if "learner" in name:
            assert param.requires_grad == True
            transformer_trainable_parameters.append(param)

    # This ema setting affects the previous 20 × 8 = 160 steps on average.
    ema = EMAModuleWrapper(transformer_trainable_parameters, decay=0.9, update_step_interval=8, device=accelerator.device)
    
    # Enable TF32 for faster training on Ampere GPUs,
    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
    if config.allow_tf32:
        torch.backends.cuda.matmul.allow_tf32 = True

    # Initialize the optimizer
    if config.train.use_8bit_adam:
        try:
            import bitsandbytes as bnb
        except ImportError:
            raise ImportError(
                "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
            )

        optimizer_cls = bnb.optim.AdamW8bit
    else:
        optimizer_cls = torch.optim.AdamW

    optimizer = optimizer_cls(
        transformer_trainable_parameters,
        lr=config.train.learning_rate,
        betas=(config.train.adam_beta1, config.train.adam_beta2),
        weight_decay=config.train.adam_weight_decay,
        eps=config.train.adam_epsilon,
    )

    # prepare prompt and reward fn
    reward_fn = getattr(flow_grpo.rewards, 'multi_score')(accelerator.device, config.reward_fn)
    eval_reward_fn = getattr(flow_grpo.rewards, 'multi_score')(accelerator.device, config.reward_fn)

    if config.prompt_fn == "general_ocr":
        train_dataset = TextPromptDataset(config.dataset, 'train')
        test_dataset = TextPromptDataset(config.dataset, 'test')

        # Create an infinite-loop DataLoader
        train_sampler = DistributedKRepeatSampler( 
            dataset=train_dataset,
            batch_size=config.sample.train_batch_size,
            k=config.sample.num_image_per_prompt,
            num_replicas=accelerator.num_processes,
            rank=accelerator.process_index,
            seed=42
        )

        # Create a DataLoader; note that shuffling is not needed here because it’s controlled by the Sampler.
        train_dataloader = DataLoader(
            train_dataset,
            batch_sampler=train_sampler,
            num_workers=1,
            collate_fn=TextPromptDataset.collate_fn,
            # persistent_workers=True
        )

        # Create a regular DataLoader
        test_dataloader = DataLoader(
            test_dataset,
            batch_size=config.sample.test_batch_size,
            collate_fn=TextPromptDataset.collate_fn,
            shuffle=False,
            num_workers=8,
        )
    
    elif config.prompt_fn == "geneval":
        train_dataset = GenevalPromptDataset(config.dataset, 'train')
        test_dataset = GenevalPromptDataset(config.dataset, 'test')

        train_sampler = DistributedKRepeatSampler( 
            dataset=train_dataset,
            batch_size=config.sample.train_batch_size,
            k=config.sample.num_image_per_prompt,
            num_replicas=accelerator.num_processes,
            rank=accelerator.process_index,
            seed=42
        )

        train_dataloader = DataLoader(
            train_dataset,
            batch_sampler=train_sampler,
            num_workers=1,
            collate_fn=GenevalPromptDataset.collate_fn,
            # persistent_workers=True
        )
        test_dataloader = DataLoader(
            test_dataset,
            batch_size=config.sample.test_batch_size,
            collate_fn=GenevalPromptDataset.collate_fn,
            shuffle=False,
            num_workers=8,
        )
    else:
        raise NotImplementedError("Only general_ocr is supported with dataset")


    neg_prompt_embed, neg_pooled_prompt_embed = compute_text_embeddings([""], text_encoders, tokenizers, max_sequence_length=128, device=accelerator.device)

    sample_neg_prompt_embeds = neg_prompt_embed.repeat(config.sample.train_batch_size, 1, 1)
    sample_neg_pooled_prompt_embeds = neg_pooled_prompt_embed.repeat(config.sample.train_batch_size, 1)

    if config.sample.num_image_per_prompt == 1:
        config.per_prompt_stat_tracking = False
    # initialize stat tracker
    if config.per_prompt_stat_tracking:
        stat_tracker = PerPromptStatTracker(config.sample.global_std)

    # for some reason, autocast is necessary for non-lora training but for lora training it isn't necessary and it uses
    # more memory
    autocast = contextlib.nullcontext if config.use_lora else accelerator.autocast
    # autocast = accelerator.autocast

    # Prepare everything with our `accelerator`.
    transformer, optimizer, train_dataloader, test_dataloader = accelerator.prepare(transformer, optimizer, train_dataloader, test_dataloader)
    noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
        config.pretrained.model, subfolder="scheduler"
    )
    # executor to perform callbacks asynchronously. this is beneficial for the llava callbacks which makes a request to a
    # remote server running llava inference.
    executor = futures.ThreadPoolExecutor(max_workers=8)

    # Train!
    samples_per_epoch = (
        config.sample.train_batch_size
        * accelerator.num_processes
        * config.sample.num_batches_per_epoch
    )
    total_train_batch_size = (
        config.train.batch_size
        * accelerator.num_processes
        * config.train.gradient_accumulation_steps
    )

    logger.info("***** Running training *****")
    logger.info(f"  Sample batch size per device = {config.sample.train_batch_size}")
    logger.info(f"  Train batch size per device = {config.train.batch_size}")
    logger.info(
        f"  Gradient Accumulation steps = {config.train.gradient_accumulation_steps}"
    )
    logger.info("")
    logger.info(f"  Total number of samples per epoch = {samples_per_epoch}")
    logger.info(
        f"  Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size}"
    )
    logger.info(
        f"  Number of gradient updates per inner epoch = {samples_per_epoch // total_train_batch_size}"
    )
    logger.info(f"  Number of inner epochs = {config.train.num_inner_epochs}")
    # assert config.sample.train_batch_size >= config.train.batch_size
    # assert config.sample.train_batch_size % config.train.batch_size == 0
    # assert samples_per_epoch % total_train_batch_size == 0

    epoch = 0
    global_step = 0
    train_iter = iter(train_dataloader)

    while True:
        #################### EVAL ####################
        pipeline.transformer.eval()
        if epoch % config.eval_freq == 0:
            eval(pipeline, test_dataloader, text_encoders, tokenizers, config, accelerator, global_step, eval_reward_fn, executor, autocast, num_train_timesteps, ema, transformer_trainable_parameters)
        if epoch % config.save_freq == 0 and epoch > 0 and accelerator.is_main_process:
            save_ckpt(config.save_dir, transformer, global_step, accelerator, ema, transformer_trainable_parameters, config)

        #################### SAMPLING ####################
        pipeline.transformer.eval()
        samples = []
        prompts = []
        for i in tqdm(
            range(config.sample.num_batches_per_epoch),
            desc=f"Epoch {epoch}: sampling",
            disable=not accelerator.is_local_main_process,
            position=0,
        ):
            train_sampler.set_epoch(epoch * config.sample.num_batches_per_epoch + i)
            prompts, prompt_metadata = next(train_iter)

            prompt_embeds, pooled_prompt_embeds = compute_text_embeddings(
                prompts, 
                text_encoders, 
                tokenizers, 
                max_sequence_length=128, 
                device=accelerator.device
            )
            prompt_ids = tokenizers[0](
                prompts,
                padding="max_length",
                max_length=256,
                truncation=True,
                return_tensors="pt",
            ).input_ids.to(accelerator.device)

            # sample
            if global_step>0 and global_step%config.train.ref_update_step==0:
                copy_learner_to_ref(transformer)
            with autocast():
                with torch.no_grad():
                    pipeline.transformer.set_adapter("ref")
                    images, latents, log_probs = pipeline_with_logprob(
                        pipeline,
                        prompt_embeds=prompt_embeds,
                        pooled_prompt_embeds=pooled_prompt_embeds,
                        negative_prompt_embeds=sample_neg_prompt_embeds,
                        negative_pooled_prompt_embeds=sample_neg_pooled_prompt_embeds,
                        num_inference_steps=config.sample.num_steps,
                        guidance_scale=config.sample.guidance_scale,
                        output_type="pt",
                        height=config.resolution,
                        width=config.resolution, 
                        noise_level=config.sample.noise_level,
                    )

            # compute rewards asynchronously
            rewards = executor.submit(reward_fn, images, prompts, prompt_metadata, only_strict=True)
            # yield to to make sure reward computation starts
            time.sleep(0)

            samples.append(
                {
                    "prompt_ids": prompt_ids,
                    "prompt_embeds": prompt_embeds,
                    "pooled_prompt_embeds": pooled_prompt_embeds,
                    "latents": latents[-1],
                    "rewards": rewards,
                }
            )

        # wait for all rewards to be computed
        for sample in tqdm(
            samples,
            desc="Waiting for rewards",
            disable=not accelerator.is_local_main_process,
            position=0,
        ):
            rewards, reward_metadata = sample["rewards"].result()
            # accelerator.print(reward_metadata)
            sample["rewards"] = {
                key: torch.as_tensor(value, device=accelerator.device).float()
                for key, value in rewards.items()
            }

        # collate samples into dict where each entry has shape (num_batches_per_epoch * sample.batch_size, ...)
        samples = {
            k: torch.cat([s[k] for s in samples], dim=0)
            if not isinstance(samples[0][k], dict)
            else {
                sub_key: torch.cat([s[k][sub_key] for s in samples], dim=0)
                for sub_key in samples[0][k]
            }
            for k in samples[0].keys()
        }

        if epoch % 10 == 0 and accelerator.is_main_process:
            # this is a hack to force wandb to log the images as JPEGs instead of PNGs
            with tempfile.TemporaryDirectory() as tmpdir:
                num_samples = min(15, len(images))
                sample_indices = random.sample(range(len(images)), num_samples)

                for idx, i in enumerate(sample_indices):
                    image = images[i]
                    pil = Image.fromarray(
                        (image.cpu().numpy().transpose(1, 2, 0) * 255).astype(np.uint8)
                    )
                    pil = pil.resize((config.resolution, config.resolution))
                    pil.save(os.path.join(tmpdir, f"{idx}.jpg"))  # 使用新的索引

                sampled_prompts = [prompts[i] for i in sample_indices]
                sampled_rewards = [rewards['avg'][i] for i in sample_indices]

                wandb.log(
                    {
                        "images": [
                            wandb.Image(
                                os.path.join(tmpdir, f"{idx}.jpg"),
                                caption=f"{prompt:.100} | avg: {avg_reward:.2f}",
                            )
                            for idx, (prompt, avg_reward) in enumerate(zip(sampled_prompts, sampled_rewards))
                        ],
                    },
                    step=global_step,
                )
        samples["rewards"]["ori_avg"] = samples["rewards"]["avg"]
        samples["rewards"]["avg"] = samples["rewards"]["avg"].unsqueeze(-1)
        
        # gather rewards across processes
        gathered_rewards = {key: accelerator.gather(value) for key, value in samples["rewards"].items()}
        gathered_rewards = {key: value.cpu().numpy() for key, value in gathered_rewards.items()}
        # log rewards and images
        if accelerator.is_main_process:
            wandb.log(
                {
                    "epoch": epoch,
                    **{f"reward_{key}": value.mean() for key, value in gathered_rewards.items() if '_strict_accuracy' not in key and '_accuracy' not in key},
                },
                step=global_step,
            )

        # per-prompt mean/std tracking
        if config.per_prompt_stat_tracking:
            # gather the prompts across processes
            prompt_ids = accelerator.gather(samples["prompt_ids"]).cpu().numpy()
            prompts = pipeline.tokenizer.batch_decode(
                prompt_ids, skip_special_tokens=True
            )
            advantages = stat_tracker.update(prompts, gathered_rewards['avg'], type=config.train.algorithm)
            if accelerator.is_local_main_process:
                print("len(prompts)", len(prompts))
                print("len unique prompts", len(set(prompts)))

            group_size, trained_prompt_num = stat_tracker.get_stats()

            zero_std_ratio, reward_std_mean = calculate_zero_std_ratio(prompts, gathered_rewards)

            if accelerator.is_main_process:
                wandb.log(
                    {
                        "group_size": group_size,
                        "trained_prompt_num": trained_prompt_num,
                        "zero_std_ratio": zero_std_ratio,
                        "reward_std_mean": reward_std_mean,
                    },
                    step=global_step,
                )
            stat_tracker.clear()
        else:
            advantages = (gathered_rewards['avg'] - gathered_rewards['avg'].mean()) / (gathered_rewards['avg'].std() + 1e-4)

        # ungather advantages; we only need to keep the entries corresponding to the samples on this process

        prompt_ids = accelerator.gather(samples["prompt_ids"]).cpu().numpy()
        latents = accelerator.gather(samples['latents']).cpu().numpy()
        prompt_embeds = accelerator.gather(samples['prompt_embeds']).cpu().numpy()
        pooled_prompt_embeds = accelerator.gather(samples['pooled_prompt_embeds']).cpu().numpy()

        # Filter out samples with non-zero advantages
        non_zero_indices = np.where(advantages != 0)[0]
        filtered_advantages = advantages[non_zero_indices]
        filtered_latents = latents[non_zero_indices]
        filtered_prompt_ids = prompt_ids[non_zero_indices]
        filtered_prompt_embeds = prompt_embeds[non_zero_indices]
        filtered_pooled_prompt_embeds = pooled_prompt_embeds[non_zero_indices]
        
        # Group latents by prompt_ids
        unique_prompt_ids = np.unique(filtered_prompt_ids, axis=0)
        concat_advantages = []
        concat_latent = []
        concat_prompt_embeds = []
        concat_pooled_prompt_embeds = []

        for prompt_id in unique_prompt_ids:
            # Find indices where prompt_id matches
            matches = np.where(np.all(filtered_prompt_ids == prompt_id, axis=1))[0]
            advantages = filtered_advantages[matches]
            latents = filtered_latents[matches]
            prompt_embeds = filtered_prompt_embeds[matches]
            pooled_prompt_embeds = filtered_pooled_prompt_embeds[matches]
            concat_advantages.append(advantages)
            concat_latent.append(latents)
            concat_prompt_embeds.append(prompt_embeds)
            concat_pooled_prompt_embeds.append(pooled_prompt_embeds)
        
        # Stack all grouped latents
        concat_advantages = np.stack(concat_advantages, axis=0)  # Shape: [num_prompts, 2, 1]
        concat_latent = np.stack(concat_latent, axis=0)  # Shape: [num_prompts, 2, 16, 64, 64]
        concat_prompt_embeds = np.stack(concat_prompt_embeds, axis=0)
        concat_pooled_prompt_embeds = np.stack(concat_pooled_prompt_embeds, axis=0)

        concat_advantages = torch.as_tensor(concat_advantages)
        concat_latent = torch.as_tensor(concat_latent)
        concat_prompt_embeds = torch.as_tensor(concat_prompt_embeds)
        concat_pooled_prompt_embeds = torch.as_tensor(concat_pooled_prompt_embeds)
        
        # This is because when handling multiple tasks, our prompt dataset contains duplicates, which leads to inconsistent group sizes.
        # Check if we have enough samples to distribute across processes
        min_required_samples = accelerator.num_processes
        current_samples = concat_advantages.shape[0]
        
        # If we don't have enough samples, randomly sample from existing ones to make it divisible
        if current_samples % min_required_samples != 0:
            samples_needed = min_required_samples - (current_samples % min_required_samples)
            # Randomly select indices to duplicate
            random_indices = torch.randint(0, current_samples, (samples_needed,))
            
            # Append the randomly sampled data to make it divisible by num_processes
            concat_advantages = torch.cat([concat_advantages, concat_advantages[random_indices]], dim=0)
            concat_latent = torch.cat([concat_latent, concat_latent[random_indices]], dim=0)
            concat_prompt_embeds = torch.cat([concat_prompt_embeds, concat_prompt_embeds[random_indices]], dim=0)
            concat_pooled_prompt_embeds = torch.cat([concat_pooled_prompt_embeds, concat_pooled_prompt_embeds[random_indices]], dim=0)

        advantages = concat_advantages.reshape(accelerator.num_processes, -1, *concat_advantages.shape[1:])[accelerator.process_index].to(accelerator.device)
        latents = concat_latent.reshape(accelerator.num_processes, -1, *concat_latent.shape[1:])[accelerator.process_index].to(accelerator.device)
        prompt_embeds = concat_prompt_embeds.reshape(accelerator.num_processes, -1, *concat_prompt_embeds.shape[1:])[accelerator.process_index].to(accelerator.device)
        pooled_prompt_embeds = concat_pooled_prompt_embeds.reshape(accelerator.num_processes, -1, *concat_pooled_prompt_embeds.shape[1:])[accelerator.process_index].to(accelerator.device)
        
        advantages = advantages.squeeze(-1)
        num_prompts = advantages.shape[0]
        for i in range(num_prompts):
            if advantages[i, 0] == -1:
                temp = latents[i, 0].clone()
                latents[i, 0] = latents[i, 1].clone()
                latents[i, 1] = temp
        
        latents = latents.permute(1,0,2,3,4)
        prompt_embeds = prompt_embeds.permute(1,0,2,3)
        pooled_prompt_embeds = pooled_prompt_embeds.permute(1,0,2)
        samples["latents"] = latents.reshape(-1,*latents.shape[2:])
        samples["prompt_embeds"] = prompt_embeds.reshape(-1,*prompt_embeds.shape[2:])
        samples["pooled_prompt_embeds"] = pooled_prompt_embeds.reshape(-1,*pooled_prompt_embeds.shape[2:])
        
        del samples["rewards"]
        del samples["prompt_ids"]

        total_batch_size = len(samples["latents"])
        num_timesteps = config.sample.num_steps

        #################### TRAINING ####################
        pipeline.transformer.set_adapter("learner")
        for inner_epoch in range(config.train.num_inner_epochs):
            # rebatch for training
            samples_batched = {
                k: v.reshape(-1, total_batch_size//config.sample.num_batches_per_epoch, *v.shape[1:])
                for k, v in samples.items()
            }

            # dict of lists -> list of dicts for easier iteration
            samples_batched = [
                dict(zip(samples_batched, x)) for x in zip(*samples_batched.values())
            ]

            # train
            pipeline.transformer.train()
            info = defaultdict(list)
            for i, sample in tqdm(
                list(enumerate(samples_batched)),
                desc=f"Epoch {epoch}.{inner_epoch}: training",
                position=0,
                disable=not accelerator.is_local_main_process,
            ):
                embeds = sample["prompt_embeds"]
                pooled_embeds = sample["pooled_prompt_embeds"]

                train_timesteps = [step_index  for step_index in range(num_train_timesteps)]
                for j in tqdm(
                    train_timesteps,
                    desc="Timestep",
                    position=1,
                    leave=False,
                    disable=not accelerator.is_local_main_process,
                ):

                    with accelerator.accumulate(transformer):
                        model_input = sample["latents"]
                        # Convert images to latent space
                        bsz = model_input.shape[0] // 2
                        noise = torch.randn_like(model_input)
                        # chosen and rejected using same noise as in Diffusion-DPO
                        noise = torch.cat([noise[:bsz], noise[:bsz]], dim=0)

                        # Sample a random timestep for each image
                        # for weighting schemes where we sample timesteps non-uniformly
                        u = compute_density_for_timestep_sampling(
                            weighting_scheme='logit_normal',
                            batch_size=bsz,
                            logit_mean=0,
                            logit_std=1,
                            mode_scale=1.29,
                        )
                        indices = (u * noise_scheduler.config.num_train_timesteps).long()
                        timesteps = noise_scheduler.timesteps[indices].to(device=model_input.device)
                        timesteps = torch.cat([timesteps, timesteps], dim=0)
                        # Add noise according to flow matching.
                        # zt = (1 - texp) * x + texp * z1
                        sigmas = get_sigmas(noise_scheduler, timesteps, accelerator, n_dim=model_input.ndim, dtype=model_input.dtype)
                        noisy_model_input = (1.0 - sigmas) * model_input + sigmas * noise
                        with autocast():
                            pipeline.transformer.set_adapter("learner")
                            model_pred = transformer(
                                hidden_states=noisy_model_input,
                                timestep=timesteps,
                                encoder_hidden_states=embeds,
                                pooled_projections=pooled_embeds,
                                return_dict=False,
                            )[0]
                            with torch.no_grad():
                                pipeline.transformer.set_adapter("ref")
                                model_pred_ref = transformer(
                                    hidden_states=noisy_model_input,
                                    timestep=timesteps,
                                    encoder_hidden_states=embeds,
                                    pooled_projections=pooled_embeds,
                                    return_dict=False,
                                )[0]
                                model_pred_ref = model_pred_ref.detach()
                                pipeline.transformer.set_adapter("learner")
                        target = noise - model_input

                        theta_mse = ((model_pred.float() - target.float()) ** 2).reshape(target.shape[0], -1).mean(dim=1)
                        ref_mse = ((model_pred_ref.float() - target.float()) ** 2).reshape(target.shape[0], -1).mean(dim=1)

                        model_w_err = theta_mse[:bsz]
                        model_l_err = theta_mse[bsz:]
                        ref_w_err = ref_mse[:bsz]
                        ref_l_err = ref_mse[bsz:]
                        w_diff = model_w_err - ref_w_err
                        l_diff = model_l_err - ref_l_err
                        w_l_diff = w_diff - l_diff
                        inside_term = -0.5 * config.train.beta * w_l_diff
                        loss = -F.logsigmoid(inside_term)

                        loss = torch.mean(loss)
                        info["loss"].append(loss)
                        info["model_w_err"].append(torch.mean(model_w_err))
                        info["model_l_err"].append(torch.mean(model_l_err))
                        info["ref_w_err"].append(torch.mean(ref_w_err))
                        info["ref_l_err"].append(torch.mean(ref_l_err))
                        info["w_diff"].append(torch.mean(w_diff))
                        info["l_diff"].append(torch.mean(l_diff))
                        info["w_l_diff"].append(torch.mean(w_l_diff))
                        info["inside_term"].append(torch.mean(inside_term))
                        implicit_acc = (inside_term > 0).sum().float() / inside_term.size(0)
                        info["implicit_acc"].append(torch.mean(implicit_acc))
                        # backward pass
                        accelerator.backward(loss)
                        if accelerator.sync_gradients:
                            accelerator.clip_grad_norm_(
                                transformer.parameters(), config.train.max_grad_norm
                            )
                        optimizer.step()
                        optimizer.zero_grad()

                    # Checks if the accelerator has performed an optimization step behind the scenes
                    if accelerator.sync_gradients:
                        # assert (j == train_timesteps[-1]) and (
                        #     i + 1
                        # ) % config.train.gradient_accumulation_steps == 0
                        # log training-related stuff
                        info = {k: torch.mean(torch.stack(v)) for k, v in info.items()}
                        info = accelerator.reduce(info, reduction="mean")
                        info.update({"epoch": epoch, "inner_epoch": inner_epoch})
                        if accelerator.is_main_process:
                            wandb.log(info, step=global_step)
                        info = defaultdict(list)
                        global_step += 1
                        if config.train.ema:
                            ema.step(transformer_trainable_parameters, global_step)
        epoch+=1    
        
if __name__ == "__main__":
    app.run(main)